Biomedical subjects

John J Heine

Publications and source records attributed to John J Heine.

6 recordsLinked to original sources

PubMed2006

Effective x-ray attenuation measurements with full field digital mammography.

This work shows that effective x-ray attenuation coefficients may be estimated by applying Beer's Law to phantom image data acquired with the General Electric Senographe 2000D full field digital mammography system. Theoretical developments are provided indicating that an approximate form of the Beer's relation holds for polychromatic x-ray beams. The theoretical values were compared with experimentally determined measured values, which were estimated at various detector locations. The measured effective attenuation coefficients are in agreement with those estimated with theoretical developments and numerical integration. The work shows that the measured quantities show little spatial variation. The main ideas are demonstrated with polymethylmethacrylate and breast tissue equivalent phantom imaging experiments. The work suggests that the effective attenuation coefficients may be used as known values for radiometric standardization applications that compensate for the image acquisition influences. The work indicates that it is possible to make quantitative attenuation coefficient measurements from a system designed for clinical purposes.

Algorithms↗

PubMed2006

Aspects of signal-dependent noise characterization.

An automated method is presented for analyzing signal-dependent noise. Signal-dependent noise is present in many types of data-acquisition processes and has been investigated by other researchers with various methods. Regardless of the noise analysis methods, often the starting point is based on a particular signal-dependent noise model, which also forms the basis for our study. The approach determines whether the estimated noise variance is dependent on the signal by approximating the functional relation within the constraints of the assumed signal-noise model. The method relies on the Fourier attributes of the signal and noise and uses the wavelet expansion for separating these components. The technique does not rely on the underlying noise and signal probability distributions. Two-dimensional simulations as well as mammography data are used to illustrate the merits of the approach.

Journal Article↗

PubMed2004

Improved interpretation of digitized mammography with wavelet processing: a localization response operating characteristic study.

OBJECTIVE: Our objective was the implementation and evaluation of a novel enhancement technique for improved interpretation of high-resolution digitized mammograms from computer monitors. MATERIALS AND METHODS: A wavelet algorithm was designed to attenuate the image spectral characteristics responsible for the long-range image correlation that often interferes with digital display. The algorithm was evaluated with a localization response operating characteristic (LROC) experiment with 500 negative, benign, and cancer cases with masses and calcification clusters. Three observers reviewed the original and wavelet-enhanced images on a 5-Mpixel monitor using a custom-made workstation user interface. RESULTS: Performance indexes were estimated for four different case combinations, each observer, and each interpretation mode. Wavelet enhancement improved the performance of all observers in all case combinations. Detection accuracy ranged from 0.678 to 0.827 for the unprocessed original data and 0.709-0.871 for the enhanced cases. Localization accuracy ranged from 0.547 to 0.785 for the original images and 0.568-0.847 for the enhanced cases, yielding increases of 5-15%. The difference between enhanced and original performances was statistically significant at the 0.10 level and in a few combinations at the 0.05 level. CONCLUSION: Soft-copy digitized mammography could replace standard film mammography under appropriate display parameters and conditions. The optimization of the soft-copy quality is expected to require more advanced processing techniques than standard gray-scale adjustments. Wavelet-based algorithms, such as the one proposed here, offer better soft-copy quality than the originals and a better starting point for additional manual gray-scale adjustments or automated postprocessing.

Algorithms↗

PubMed2002

Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 1. Tissue and related risk factors.

This work is presented as a sequence of two parts. In this leading section, a review of the breast tissue-risk research is provided. Although controversy remains, there is substantial evidence indicating that dense mammographic tissue (a) is a breast cancer risk factor that is at least similar, if not greater, in magnitude with the other known breast cancer risk factors and (b) may be a partial biomarker for some of the other risk factors. Understanding these influences may provide a mechanism for measuring the dynamics of breast cancer risk. The totality of this work is to provide support for an automated serial mammography study under way at the authors' institution, where digital mammographic images are acquired with a full-field digital mammography system. This is a filmless imaging system, where the image is acquired in digital format. This electronic imaging acquisition system provides a prime opportunity to easily couple and manipulate the image data with patient information such as risk probability analysis or other pertinent personal history data for improved automated decision making. In this leading section, the main focus is on understanding elements that will assist in fusing risk probability analysis with automated computer-aided diagnosis. The evidence indicates that there are many factors that influence breast tissue at any given time and thus have the ability to alter the associated radiographic image appearance over time. At the initiation of the serial study it was clear that the authors did not fully understand the nature of the problem: automatically comparing similar mammographic scenes acquired at different times. In the second part of this sequence, the more time-related tissue influences are reviewed.

Breast↗

PubMed2002

Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 2. Serial breast tissue change and related temporal influences.

The work presented herein is the second part of a review of breast tissue-related cancer-risk research. Briefly, in part 1, the tissue-risk research is discussed. In this part, factors that influence temporal breast tissue change are reviewed. Since breast composition is correlated with some of the known risk factors, understanding these influences may provide a mechanism for measuring the dynamics of breast cancer risk. The purpose of this work is to provide support for an automated serial mammography study under way at the authors' institution, where the digital mammographic images are acquired with a full-field digital mammography imaging system. At the initiation of the serial study, it was clear that the authors did not fully understand the nature of the problem: automatically comparing similar mammographic scenes acquired at different times. The evidence indicates that there are many factors that influence breast tissue at any given time and thus have the ability to alter the associated radiographic appearance over time. In general, the topics considered herein include aging; involution; breast development; exogenous and endogenous hormonal interactions such as hormone replacement therapy, oral contraceptive use, and menstrual timing; screening sensitivity issues and interval cancers; tumor growth rates; sojourn times; and lifestyle factors such as diet and exercise. Throughout this work, commentaries and suggestive strategies for automated serial image analysis are provided.

Aging↗

PubMed2002

Spectral analysis of full field digital mammography data.

The spectral content of mammograms acquired from using a full field digital mammography (FFDM) system are analyzed. Fourier methods are used to show that the FFDM image power spectra obey an inverse power law; in an average sense, the images may be considered as 1/f fields. Two data representations are analyzed and compared (1) the raw data, and (2) the logarithm of the raw data. Two methods are employed to analyze the power spectra (1) a technique based on integrating the Fourier plane with octave ring sectioning developed previously, and (2) an approach based on integrating the Fourier plane using rings of constant width developed for this work. Both methods allow theoretical modeling. Numerical analysis indicates that the effects due to the transformation influence the power spectra measurements in a statistically significant manner in the high frequency range. However, this effect has little influence on the inverse power law estimation for a given image regardless of the data representation or the theoretical analysis approach. The analysis is presented from two points of view (1) each image is treated independently with the results presented as distributions, and (2) for a given representation, the entire image collection is treated as an ensemble with the results presented as expected values. In general, the constant ring width analysis forms the foundation for a spectral comparison method for finding spectral differences, from an image distribution sense, after applying a nonlinear transformation to the data. The work also shows that power law estimation may be influenced due to the presence of noise in the higher frequency range, which is consistent with the known attributes of the detector efficiency. The spectral modeling and inverse power law determinations obtained here are in agreement with that obtained from the analysis of digitized film-screen images presented previously. The form of the power spectrum for a given image is approximately l/f2beta with beta approximately 1.4-1.5.

Biophysical Phenomena↗